Conference School Area Classification (0-Rural; 1-Urban)
ACC :230 Michigan :143 0: 149
Big10:594 Ohio State :123 1:1242
Big12: 77 UNC :100
Pac12:221 Penn State : 82
SEC :269 Tennessee : 77
Colorado University: 59
(Other) :807
Year Tenure Year In-Season_Game S_Diversion
Min. :2003 Min. : 1.000 Min. :1.000 Min. :0.0000
1st Qu.:2013 1st Qu.: 2.000 1st Qu.:2.000 1st Qu.:0.1585
Median :2016 Median : 4.000 Median :4.000 Median :0.2984
Mean :2015 Mean : 5.958 Mean :3.867 Mean :0.3996
3rd Qu.:2018 3rd Qu.: 9.000 3rd Qu.:6.000 3rd Qu.:0.6770
Max. :2024 Max. :20.000 Max. :9.000 Max. :0.9868
Attendance Game Time Game result (Win=1; Loss=0)
Min. : 1275 12:00 :357 0 :416
1st Qu.: 48542 15:30 :248 1 :974
Median : 78556 19:30 :131 NA's: 1
Mean : 73502 19:00 :101
3rd Qu.:101762 12:30 : 71
Max. :115109 20:00 : 62
NA's :1 (Other):421
Athletic Dept Profit Athletic Dept Total Expenses Athletic Dept Total Revenues
Min. :-64721133 Min. : 48207321 Min. : 47191240
1st Qu.: 0 1st Qu.: 86924779 1st Qu.: 89079982
Median : 1763077 Median :115349117 Median :122739052
Mean : 4407879 Mean :120151264 Mean :125072097
3rd Qu.: 8224233 3rd Qu.:139798191 3rd Qu.:147511034
Max. : 65413805 Max. :327782612 Max. :331905866
NA's :145 NA's :145
skim(data_clean)
Data summary
Name
data_clean
Number of rows
1391
Number of columns
13
_______________________
Column type frequency:
factor
5
numeric
8
________________________
Group variables
None
Variable type: factor
skim_variable
n_missing
complete_rate
ordered
n_unique
top_counts
Conference
0
1
FALSE
5
Big: 594, SEC: 269, ACC: 230, Pac: 221
School
0
1
FALSE
31
Mic: 143, Ohi: 123, UNC: 100, Pen: 82
Area Classification (0-Rural; 1-Urban)
0
1
FALSE
2
1: 1242, 0: 149
Game Time
0
1
FALSE
54
12:: 357, 15:: 248, 19:: 131, 19:: 101
Game result (Win=1; Loss=0)
1
1
FALSE
2
1: 974, 0: 416
Variable type: numeric
skim_variable
n_missing
complete_rate
mean
sd
p0
p25
p50
p75
p100
hist
Year
0
1.0
2015.19
4.61
2003
2013.00
2016.0
2018.00
2024.00
▂▂▅▇▂
Tenure Year
0
1.0
5.96
4.64
1
2.00
4.0
9.00
20.00
▇▃▂▂▁
In-Season_Game
0
1.0
3.87
1.98
1
2.00
4.0
6.00
9.00
▇▇▃▆▁
S_Diversion
0
1.0
0.40
0.29
0
0.16
0.3
0.68
0.99
▇▆▃▃▃
Attendance
1
1.0
73502.37
28000.27
1275
48541.75
78556.0
101762.00
115109.00
▁▅▅▅▇
Athletic Dept Profit
0
1.0
4407878.58
12982815.85
-64721133
0.00
1763077.0
8224233.00
65413805.00
▁▁▇▂▁
Athletic Dept Total Expenses
145
0.9
120151263.53
46373051.82
48207321
86924779.00
115349117.0
139798191.00
327782612.00
▇▇▁▁▁
Athletic Dept Total Revenues
145
0.9
125072097.49
48770769.83
47191240
89079982.00
122739052.0
147511034.00
331905866.00
▇▇▂▁▁
summary_df <- summarytools::dfSummary(data_clean,varnumbers=FALSE,plain.ascii=FALSE,style="grid",graph.col =TRUE,valid.col=FALSE)# Print the summary table and suppress warningsprint(summary_df,method="render",table.classes="table-condensed")
Data Frame Summary
data_clean
Dimensions: 1391 x 13
Duplicates: 0
Variable
Stats / Values
Freqs (% of Valid)
Graph
Missing
Conference [factor]
1. ACC
2. Big10
3. Big12
4. Pac12
5. SEC
230
(
16.5%
)
594
(
42.7%
)
77
(
5.5%
)
221
(
15.9%
)
269
(
19.3%
)
0 (0.0%)
School [factor]
1. Arizona State
2. Arkansas
3. Auburn
4. Clemson
5. Colorado University
6. Duke
7. Florida
8. Georgia
9. Georgia Tech
10. Illinois
[ 21 others ]
33
(
2.4%
)
6
(
0.4%
)
36
(
2.6%
)
28
(
2.0%
)
59
(
4.2%
)
38
(
2.7%
)
43
(
3.1%
)
36
(
2.6%
)
33
(
2.4%
)
28
(
2.0%
)
1051
(
75.6%
)
0 (0.0%)
Area Classification (0-Rural; 1-Urban) [factor]
1. 0
2. 1
149
(
10.7%
)
1242
(
89.3%
)
0 (0.0%)
Year [numeric]
Mean (sd) : 2015.2 (4.6)
min ≤ med ≤ max:
2003 ≤ 2016 ≤ 2024
IQR (CV) : 5 (0)
22 distinct values
0 (0.0%)
Tenure Year [numeric]
Mean (sd) : 6 (4.6)
min ≤ med ≤ max:
1 ≤ 4 ≤ 20
IQR (CV) : 7 (0.8)
20 distinct values
0 (0.0%)
In-Season_Game [numeric]
Mean (sd) : 3.9 (2)
min ≤ med ≤ max:
1 ≤ 4 ≤ 9
IQR (CV) : 4 (0.5)
1
:
209
(
15.0%
)
2
:
212
(
15.2%
)
3
:
210
(
15.1%
)
4
:
206
(
14.8%
)
5
:
204
(
14.7%
)
6
:
199
(
14.3%
)
7
:
131
(
9.4%
)
8
:
19
(
1.4%
)
9
:
1
(
0.1%
)
0 (0.0%)
S_Diversion [numeric]
Mean (sd) : 0.4 (0.3)
min ≤ med ≤ max:
0 ≤ 0.3 ≤ 1
IQR (CV) : 0.5 (0.7)
1301 distinct values
0 (0.0%)
Attendance [numeric]
Mean (sd) : 73502.4 (28000.3)
min ≤ med ≤ max:
1275 ≤ 78556 ≤ 115109
IQR (CV) : 53220.2 (0.4)
1200 distinct values
1 (0.1%)
Game Time [factor]
1. 09:00
2. 10:00
3. 11:00
4. 11:05
5. 11:30
6. 12:00
7. 12:05
8. 12:10
9. 12:15
10. 12:20
[ 44 others ]
1
(
0.1%
)
2
(
0.1%
)
58
(
4.2%
)
2
(
0.1%
)
7
(
0.5%
)
357
(
25.7%
)
1
(
0.1%
)
2
(
0.1%
)
1
(
0.1%
)
17
(
1.2%
)
943
(
67.8%
)
0 (0.0%)
Game result (Win=1; Loss=0) [factor]
1. 0
2. 1
416
(
29.9%
)
974
(
70.1%
)
1 (0.1%)
Athletic Dept Profit [numeric]
Mean (sd) : 4407879 (12982816)
min ≤ med ≤ max:
-64721133 ≤ 1763077 ≤ 65413805
IQR (CV) : 8224233 (2.9)
187 distinct values
0 (0.0%)
Athletic Dept Total Expenses [numeric]
Mean (sd) : 120151264 (46373052)
min ≤ med ≤ max:
48207321 ≤ 115349117 ≤ 327782612
IQR (CV) : 52873412 (0.4)
189 distinct values
145 (10.4%)
Athletic Dept Total Revenues [numeric]
Mean (sd) : 125072098 (48770770)
min ≤ med ≤ max:
47191240 ≤ 122739052 ≤ 331905866
IQR (CV) : 58431052 (0.4)
189 distinct values
145 (10.4%)
Generated by summarytools 1.1.4 (R version 4.5.1) 2025-10-22
Focous on missing data
Attendance
Attendance_Missing <-subset(data_clean, is.na(`Attendance`)) ## This one is cancelledprint.data.frame (Attendance_Missing)
Conference School Area Classification (0-Rural; 1-Urban) Year Tenure Year
1 SEC LSU 1 2015 1
In-Season_Game S_Diversion Attendance Game Time Game result (Win=1; Loss=0)
1 1 0.2034806 NA 19:30 <NA>
Athletic Dept Profit Athletic Dept Total Expenses
1 12009860 126632377
Athletic Dept Total Revenues
1 138642237
Game result (Win=1; Loss=0)
Game_result_Missing <-subset(data_clean, is.na(`Game result (Win=1; Loss=0)`))print.data.frame (Game_result_Missing)
Conference School Area Classification (0-Rural; 1-Urban) Year Tenure Year
1 SEC LSU 1 2015 1
In-Season_Game S_Diversion Attendance Game Time Game result (Win=1; Loss=0)
1 1 0.2034806 NA 19:30 <NA>
Athletic Dept Profit Athletic Dept Total Expenses
1 12009860 126632377
Athletic Dept Total Revenues
1 138642237
data_clean <-subset(data_clean, !is.na(`Game result (Win=1; Loss=0)`))
subset(data_clean, is.na(`Game result (Win=1; Loss=0)`))
# A tibble: 0 × 13
# ℹ 13 variables: Conference <fct>, School <fct>,
# Area Classification (0-Rural; 1-Urban) <fct>, Year <dbl>,
# Tenure Year <dbl>, In-Season_Game <dbl>, S_Diversion <dbl>,
# Attendance <dbl>, Game Time <fct>, Game result (Win=1; Loss=0) <fct>,
# Athletic Dept Profit <dbl>, Athletic Dept Total Expenses <dbl>,
# Athletic Dept Total Revenues <dbl>
Athletic Dept Total Expenses
Athletic_Dept_Total_Expenses_Missing <-subset(data_clean, is.na(`Athletic Dept Total Expenses`))print.data.frame (Athletic_Dept_Total_Expenses_Missing)
Conference School Area Classification (0-Rural; 1-Urban) Year
1 ACC Duke 1 2014
2 ACC Duke 1 2014
3 ACC Duke 1 2014
4 ACC Duke 1 2014
5 ACC Duke 1 2014
6 ACC Duke 1 2014
7 ACC Duke 1 2014
8 ACC Duke 1 2015
9 ACC Duke 1 2015
10 ACC Duke 1 2015
11 ACC Duke 1 2015
12 ACC Duke 1 2015
13 ACC Duke 1 2015
14 ACC Duke 1 2016
15 ACC Duke 1 2016
16 ACC Duke 1 2016
17 ACC Duke 1 2016
18 ACC Duke 1 2016
19 ACC Duke 1 2016
20 ACC Duke 1 2017
21 ACC Duke 1 2017
22 ACC Duke 1 2017
23 ACC Duke 1 2017
24 ACC Duke 1 2017
25 ACC Duke 1 2017
26 ACC Duke 1 2017
27 ACC Duke 1 2018
28 ACC Duke 1 2018
29 ACC Duke 1 2018
30 ACC Duke 1 2018
31 ACC Duke 1 2018
32 ACC Duke 1 2018
33 ACC Duke 1 2019
34 ACC Duke 1 2019
35 ACC Duke 1 2019
36 ACC Duke 1 2019
37 ACC Duke 1 2019
38 ACC Duke 1 2019
39 Big10 Michigan 1 2004
40 Big10 Michigan 1 2004
41 Big10 Michigan 1 2004
42 Big10 Michigan 1 2004
43 Big10 Michigan 1 2004
44 Big10 Michigan 1 2004
45 Big10 Penn State 1 2009
46 Big10 Penn State 1 2009
47 Big10 Penn State 1 2009
48 Big10 Penn State 1 2009
49 Big10 Penn State 1 2009
50 Big10 Penn State 1 2009
51 Big10 Penn State 1 2009
52 Big10 Penn State 1 2009
53 Big10 Penn State 1 2010
54 Big10 Penn State 1 2010
55 Big10 Penn State 1 2010
56 Big10 Penn State 1 2010
57 Big10 Penn State 1 2010
58 Big10 Penn State 1 2010
59 Big10 Penn State 1 2010
60 Pac12 Stanford 0 2015
61 Pac12 Stanford 0 2015
62 Pac12 Stanford 0 2015
63 Pac12 Stanford 0 2015
64 Pac12 Stanford 0 2015
65 Pac12 Stanford 0 2015
66 Pac12 Stanford 0 2016
67 Pac12 Stanford 0 2016
68 Pac12 Stanford 0 2016
69 Pac12 Stanford 0 2016
70 Pac12 Stanford 0 2016
71 Pac12 Stanford 0 2016
72 Pac12 Stanford 0 2015
73 Pac12 Stanford 0 2017
74 Pac12 Stanford 0 2017
75 Pac12 Stanford 0 2017
76 Pac12 Stanford 0 2017
77 Pac12 Stanford 0 2017
78 Pac12 Stanford 0 2017
79 Pac12 Stanford 0 2018
80 Pac12 Stanford 0 2018
81 Pac12 Stanford 0 2018
82 Pac12 Stanford 0 2018
83 Pac12 Stanford 0 2018
84 Pac12 Stanford 0 2018
85 Pac12 Stanford 0 2019
86 Pac12 Stanford 0 2019
87 Pac12 Stanford 0 2019
88 Pac12 Stanford 0 2019
89 Pac12 Stanford 0 2019
90 Pac12 Stanford 0 2019
91 Pac12 Stanford 0 2019
92 ACC UNC 1 2004
93 ACC UNC 1 2004
94 ACC UNC 1 2004
95 ACC UNC 1 2004
96 ACC UNC 1 2004
97 ACC UNC 1 2004
98 Pac12 USC 1 2015
99 Pac12 USC 1 2015
100 Pac12 USC 1 2015
101 Pac12 USC 1 2015
102 Pac12 USC 1 2015
103 Pac12 USC 1 2015
104 Pac12 USC 1 2015
105 Pac12 USC 1 2019
106 Pac12 USC 1 2019
107 Pac12 USC 1 2019
108 Pac12 USC 1 2019
109 Pac12 USC 1 2019
110 Pac12 USC 1 2019
111 Pac12 USC 1 2020
112 Pac12 USC 1 2020
113 Pac12 USC 1 2020
114 Pac12 Washington 1 2003
115 Pac12 Washington 1 2003
116 Pac12 Washington 1 2003
117 Pac12 Washington 1 2003
118 Pac12 Washington 1 2003
119 Pac12 Washington 1 2003
120 Pac12 Washington 1 2003
121 Pac12 Washington 1 2004
122 Pac12 Washington 1 2004
123 Pac12 Washington 1 2004
124 Pac12 Washington 1 2004
125 Pac12 Washington 1 2004
126 Pac12 Washington 1 2004
127 Pac12 Washington 1 2005
128 Pac12 Washington 1 2005
129 Pac12 Washington 1 2005
130 Pac12 Washington 1 2005
131 Pac12 Washington 1 2005
132 Pac12 Washington 1 2005
133 Pac12 Washington 1 2006
134 Pac12 Washington 1 2006
135 Pac12 Washington 1 2006
136 Pac12 Washington 1 2006
137 Pac12 Washington 1 2006
138 Pac12 Washington 1 2006
139 Pac12 Washington 1 2007
140 Pac12 Washington 1 2007
141 Pac12 Washington 1 2007
142 Pac12 Washington 1 2007
143 Pac12 Washington 1 2007
144 Pac12 Washington 1 2007
145 Pac12 Washington 1 2007
Tenure Year In-Season_Game S_Diversion Attendance Game Time
1 1 1 0.45965770 31213 18:00
2 1 2 0.55328798 25203 15:00
3 1 3 0.38927098 20197 12:30
4 1 4 0.44155844 28131 12:30
5 1 5 0.44279661 30107 12:00
6 1 6 0.65346535 33941 19:30
7 1 7 0.41448382 22246 19:00
8 2 1 0.35403727 33941 18:00
9 2 2 0.23130435 24127 12:30
10 2 3 0.45177665 20101 12:00
11 2 4 0.47011309 20009 15:00
12 2 5 0.71062547 30143 19:00
13 2 6 0.93586498 30241 12:00
14 3 1 0.78947368 35049 18:00
15 3 2 0.92653673 21077 15:00
16 3 3 0.78240741 25201 12:30
17 3 4 0.67702936 20613 15:30
18 3 5 0.79058824 38217 15:30
19 3 6 0.73416507 39212 19:30
20 4 1 0.68011958 30477 18:00
21 4 2 0.73101266 20241 12:00
22 4 3 0.78539493 26714 12:30
23 4 4 0.68818381 36314 19:00
24 4 5 0.76634214 31073 12:00
25 4 6 0.76619633 22621 12:20
26 4 7 0.83745583 20141 15:30
27 5 1 0.84955534 26017 19:00
28 5 2 0.69892580 30477 15:30
29 5 3 0.28565683 32177 19:00
30 5 4 0.82361005 20277 12:30
31 5 5 0.84183364 35493 12:20
32 5 6 0.76399322 20782 12:30
33 6 1 0.79851028 38313 18:00
34 6 2 0.86444975 22610 20:00
35 6 3 0.81692195 21741 12:30
36 6 4 0.79744735 40004 19:30
37 6 5 0.80054485 16286 16:00
38 6 6 0.69823789 15913 15:30
39 1 1 0.22668452 110815 12:00
40 1 2 0.22724735 109432 15:30
41 1 3 0.27982408 111428 15:30
42 1 4 0.20168067 111518 12:00
43 1 5 0.19452391 111609 15:30
44 1 6 0.16226979 111347 12:10
45 1 1 0.20416814 104968 12:00
46 1 2 0.14190507 106387 12:00
47 1 3 0.22660885 105514 15:30
48 1 4 0.13633213 109316 20:00
49 1 5 0.17337627 104488 12:00
50 1 6 0.13425000 107981 15:30
51 1 7 0.12309608 110033 15:30
52 1 8 0.14648760 107379 12:00
53 2 1 0.13005902 101213 12:00
54 2 2 0.17207665 100610 12:00
55 2 3 0.22771588 104840 15:30
56 2 4 0.19871122 107638 12:00
57 2 5 0.15857571 108539 20:00
58 2 6 0.17650602 104147 15:30
59 2 7 0.20653477 102649 12:00
60 1 1 0.08315098 51424 19:30
61 1 2 0.06550388 39100 19:30
62 1 3 0.14443885 51424 19:30
63 1 4 0.18003565 50846 19:30
64 1 5 0.10875650 48602 16:30
65 1 6 0.11517672 51424 19:30
66 2 1 0.12537092 46147 18:00
67 2 2 0.19192358 48763 17:00
68 2 3 0.30943026 33529 19:30
69 2 4 0.24571429 44535 12:00
70 2 5 0.28810573 38813 12:00
71 2 6 0.14048780 36171 17:00
72 2 7 0.12976070 51424 16:30
73 3 1 0.29346388 48042 19:30
74 3 2 0.41507510 44422 13:00
75 3 3 0.48589147 48559 20:00
76 3 4 0.56826568 44559 19:30
77 3 5 0.42449726 51424 17:00
78 3 6 0.40105715 47352 17:00
79 4 1 0.57912746 40913 18:00
80 4 2 0.29139073 42856 17:30
81 4 3 0.46209386 31772 11:00
82 4 4 0.65279770 37244 19:30
83 4 5 0.51059322 39596 16:00
84 4 6 0.43333333 34671 18:00
85 5 1 0.42704626 37179 13:00
86 5 2 0.45263158 39249 16:00
87 5 3 0.58174905 33225 19:30
88 5 4 0.41216216 31464 18:00
89 5 5 0.44992526 31711 12:30
90 5 6 0.50000000 48904 13:00
91 5 7 0.44674086 37391 13:00
92 1 1 0.42931937 43500 13:30
93 1 2 0.42303173 46250 18:00
94 1 3 0.44303797 49000 13:30
95 1 4 0.43637847 60000 18:00
96 1 5 0.44019139 58000 19:00
97 1 6 0.40165441 58000 12:00
98 1 1 0.73089564 79809 20:00
99 1 2 0.74991027 72422 17:00
100 1 3 0.85296641 78306 17:00
101 1 4 0.83716421 63623 18:00
102 1 5 0.63879480 73435 16:30
103 1 6 0.78256829 76309 19:30
104 1 7 0.89491992 83602 12:30
105 5 1 0.84638301 48746 19:30
106 5 2 0.83367456 49835 19:30
107 5 3 0.89519024 45218 12:30
108 5 4 0.90120746 43181 18:30
109 5 5 0.92817982 49922 17:00
110 5 6 0.93120393 56710 12:30
111 6 1 0.79287216 54130 09:00
112 6 2 0.66666667 50775 16:30
113 6 3 0.82990991 45928 17:00
114 1 1 0.10252193 71125 13:00
115 1 2 0.11477411 71178 13:00
116 1 3 0.10577390 71875 12:30
117 1 4 0.10441767 70149 12:30
118 1 5 0.14401914 72015 12:30
119 1 6 0.10453118 72450 19:00
120 1 7 0.12658782 74549 15:30
121 2 1 0.15600394 65345 14:30
122 2 2 0.14525433 65235 16:00
123 2 3 0.23240833 65816 12:30
124 2 4 0.12817148 65351 12:30
125 2 5 0.14147910 63225 12:30
126 2 6 0.18232891 65451 12:30
127 3 1 0.14491844 57775 12:30
128 3 2 0.18322763 61183 12:30
129 3 3 0.18818565 71473 12:30
130 3 4 0.17925793 64096 12:30
131 3 5 0.16376663 60717 15:30
132 3 6 0.20824216 70713 12:15
133 4 1 0.23236741 52256 12:30
134 4 2 0.28983764 57012 12:30
135 4 3 0.29229963 58255 12:30
136 4 4 0.27884615 62656 15:30
137 4 5 0.30752334 58822 16:00
138 4 6 0.27132262 55896 12:30
139 5 1 0.29963009 70045 12:30
140 5 2 0.28233749 74927 12:30
141 5 3 0.27479407 68654 17:00
142 5 4 0.26198565 66481 16:30
143 5 5 0.26469160 61124 12:00
144 5 6 0.26849894 60005 12:30
145 5 7 0.22701504 72888 16:00
Game result (Win=1; Loss=0) Athletic Dept Profit
1 1 0
2 1 0
3 1 0
4 1 0
5 0 0
6 0 0
7 1 0
8 1 0
9 0 0
10 1 0
11 1 0
12 0 0
13 0 0
14 1 0
15 0 0
16 0 0
17 1 0
18 0 0
19 1 0
20 1 0
21 1 0
22 1 0
23 0 0
24 0 0
25 0 0
26 1 0
27 1 0
28 1 0
29 0 0
30 0 0
31 1 0
32 0 0
33 1 0
34 0 0
35 1 0
36 0 0
37 0 0
38 1 0
39 1 0
40 1 0
41 1 0
42 1 0
43 1 0
44 1 0
45 1 0
46 1 0
47 1 0
48 0 0
49 1 0
50 1 0
51 0 0
52 1 0
53 1 0
54 1 0
55 1 0
56 0 0
57 1 0
58 1 0
59 0 0
60 1 0
61 1 0
62 1 0
63 1 0
64 0 0
65 1 0
66 1 0
67 1 0
68 0 0
69 0 0
70 1 0
71 1 0
72 1 0
73 1 0
74 1 0
75 1 0
76 1 0
77 1 0
78 1 0
79 1 0
80 1 0
81 1 0
82 0 0
83 0 0
84 1 0
85 1 0
86 0 0
87 1 0
88 0 0
89 1 0
90 0 0
91 0 0
92 1 0
93 1 0
94 0 0
95 1 0
96 1 0
97 0 0
98 1 0
99 1 0
100 0 0
101 0 0
102 1 0
103 1 0
104 1 0
105 1 0
106 1 0
107 1 0
108 1 0
109 0 0
110 1 0
111 1 0
112 1 0
113 0 0
114 1 0
115 1 0
116 1 0
117 0 0
118 0 0
119 1 0
120 1 0
121 0 0
122 0 0
123 1 0
124 0 0
125 0 0
126 0 0
127 0 0
128 1 0
129 0 0
130 0 0
131 0 0
132 0 0
133 1 0
134 1 0
135 1 0
136 0 0
137 0 0
138 0 0
139 1 0
140 0 0
141 0 0
142 0 0
143 0 0
144 1 0
145 0 0
Athletic Dept Total Expenses Athletic Dept Total Revenues
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
101 NA NA
102 NA NA
103 NA NA
104 NA NA
105 NA NA
106 NA NA
107 NA NA
108 NA NA
109 NA NA
110 NA NA
111 NA NA
112 NA NA
113 NA NA
114 NA NA
115 NA NA
116 NA NA
117 NA NA
118 NA NA
119 NA NA
120 NA NA
121 NA NA
122 NA NA
123 NA NA
124 NA NA
125 NA NA
126 NA NA
127 NA NA
128 NA NA
129 NA NA
130 NA NA
131 NA NA
132 NA NA
133 NA NA
134 NA NA
135 NA NA
136 NA NA
137 NA NA
138 NA NA
139 NA NA
140 NA NA
141 NA NA
142 NA NA
143 NA NA
144 NA NA
145 NA NA
Athletic Dept Total Revenues
Athletic_Dept_Total_Revenues_Missing <-subset(data_clean, is.na(`Athletic Dept Total Revenues`))print.data.frame (Athletic_Dept_Total_Revenues_Missing)
Conference School Area Classification (0-Rural; 1-Urban) Year
1 ACC Duke 1 2014
2 ACC Duke 1 2014
3 ACC Duke 1 2014
4 ACC Duke 1 2014
5 ACC Duke 1 2014
6 ACC Duke 1 2014
7 ACC Duke 1 2014
8 ACC Duke 1 2015
9 ACC Duke 1 2015
10 ACC Duke 1 2015
11 ACC Duke 1 2015
12 ACC Duke 1 2015
13 ACC Duke 1 2015
14 ACC Duke 1 2016
15 ACC Duke 1 2016
16 ACC Duke 1 2016
17 ACC Duke 1 2016
18 ACC Duke 1 2016
19 ACC Duke 1 2016
20 ACC Duke 1 2017
21 ACC Duke 1 2017
22 ACC Duke 1 2017
23 ACC Duke 1 2017
24 ACC Duke 1 2017
25 ACC Duke 1 2017
26 ACC Duke 1 2017
27 ACC Duke 1 2018
28 ACC Duke 1 2018
29 ACC Duke 1 2018
30 ACC Duke 1 2018
31 ACC Duke 1 2018
32 ACC Duke 1 2018
33 ACC Duke 1 2019
34 ACC Duke 1 2019
35 ACC Duke 1 2019
36 ACC Duke 1 2019
37 ACC Duke 1 2019
38 ACC Duke 1 2019
39 Big10 Michigan 1 2004
40 Big10 Michigan 1 2004
41 Big10 Michigan 1 2004
42 Big10 Michigan 1 2004
43 Big10 Michigan 1 2004
44 Big10 Michigan 1 2004
45 Big10 Penn State 1 2009
46 Big10 Penn State 1 2009
47 Big10 Penn State 1 2009
48 Big10 Penn State 1 2009
49 Big10 Penn State 1 2009
50 Big10 Penn State 1 2009
51 Big10 Penn State 1 2009
52 Big10 Penn State 1 2009
53 Big10 Penn State 1 2010
54 Big10 Penn State 1 2010
55 Big10 Penn State 1 2010
56 Big10 Penn State 1 2010
57 Big10 Penn State 1 2010
58 Big10 Penn State 1 2010
59 Big10 Penn State 1 2010
60 Pac12 Stanford 0 2015
61 Pac12 Stanford 0 2015
62 Pac12 Stanford 0 2015
63 Pac12 Stanford 0 2015
64 Pac12 Stanford 0 2015
65 Pac12 Stanford 0 2015
66 Pac12 Stanford 0 2016
67 Pac12 Stanford 0 2016
68 Pac12 Stanford 0 2016
69 Pac12 Stanford 0 2016
70 Pac12 Stanford 0 2016
71 Pac12 Stanford 0 2016
72 Pac12 Stanford 0 2015
73 Pac12 Stanford 0 2017
74 Pac12 Stanford 0 2017
75 Pac12 Stanford 0 2017
76 Pac12 Stanford 0 2017
77 Pac12 Stanford 0 2017
78 Pac12 Stanford 0 2017
79 Pac12 Stanford 0 2018
80 Pac12 Stanford 0 2018
81 Pac12 Stanford 0 2018
82 Pac12 Stanford 0 2018
83 Pac12 Stanford 0 2018
84 Pac12 Stanford 0 2018
85 Pac12 Stanford 0 2019
86 Pac12 Stanford 0 2019
87 Pac12 Stanford 0 2019
88 Pac12 Stanford 0 2019
89 Pac12 Stanford 0 2019
90 Pac12 Stanford 0 2019
91 Pac12 Stanford 0 2019
92 ACC UNC 1 2004
93 ACC UNC 1 2004
94 ACC UNC 1 2004
95 ACC UNC 1 2004
96 ACC UNC 1 2004
97 ACC UNC 1 2004
98 Pac12 USC 1 2015
99 Pac12 USC 1 2015
100 Pac12 USC 1 2015
101 Pac12 USC 1 2015
102 Pac12 USC 1 2015
103 Pac12 USC 1 2015
104 Pac12 USC 1 2015
105 Pac12 USC 1 2019
106 Pac12 USC 1 2019
107 Pac12 USC 1 2019
108 Pac12 USC 1 2019
109 Pac12 USC 1 2019
110 Pac12 USC 1 2019
111 Pac12 USC 1 2020
112 Pac12 USC 1 2020
113 Pac12 USC 1 2020
114 Pac12 Washington 1 2003
115 Pac12 Washington 1 2003
116 Pac12 Washington 1 2003
117 Pac12 Washington 1 2003
118 Pac12 Washington 1 2003
119 Pac12 Washington 1 2003
120 Pac12 Washington 1 2003
121 Pac12 Washington 1 2004
122 Pac12 Washington 1 2004
123 Pac12 Washington 1 2004
124 Pac12 Washington 1 2004
125 Pac12 Washington 1 2004
126 Pac12 Washington 1 2004
127 Pac12 Washington 1 2005
128 Pac12 Washington 1 2005
129 Pac12 Washington 1 2005
130 Pac12 Washington 1 2005
131 Pac12 Washington 1 2005
132 Pac12 Washington 1 2005
133 Pac12 Washington 1 2006
134 Pac12 Washington 1 2006
135 Pac12 Washington 1 2006
136 Pac12 Washington 1 2006
137 Pac12 Washington 1 2006
138 Pac12 Washington 1 2006
139 Pac12 Washington 1 2007
140 Pac12 Washington 1 2007
141 Pac12 Washington 1 2007
142 Pac12 Washington 1 2007
143 Pac12 Washington 1 2007
144 Pac12 Washington 1 2007
145 Pac12 Washington 1 2007
Tenure Year In-Season_Game S_Diversion Attendance Game Time
1 1 1 0.45965770 31213 18:00
2 1 2 0.55328798 25203 15:00
3 1 3 0.38927098 20197 12:30
4 1 4 0.44155844 28131 12:30
5 1 5 0.44279661 30107 12:00
6 1 6 0.65346535 33941 19:30
7 1 7 0.41448382 22246 19:00
8 2 1 0.35403727 33941 18:00
9 2 2 0.23130435 24127 12:30
10 2 3 0.45177665 20101 12:00
11 2 4 0.47011309 20009 15:00
12 2 5 0.71062547 30143 19:00
13 2 6 0.93586498 30241 12:00
14 3 1 0.78947368 35049 18:00
15 3 2 0.92653673 21077 15:00
16 3 3 0.78240741 25201 12:30
17 3 4 0.67702936 20613 15:30
18 3 5 0.79058824 38217 15:30
19 3 6 0.73416507 39212 19:30
20 4 1 0.68011958 30477 18:00
21 4 2 0.73101266 20241 12:00
22 4 3 0.78539493 26714 12:30
23 4 4 0.68818381 36314 19:00
24 4 5 0.76634214 31073 12:00
25 4 6 0.76619633 22621 12:20
26 4 7 0.83745583 20141 15:30
27 5 1 0.84955534 26017 19:00
28 5 2 0.69892580 30477 15:30
29 5 3 0.28565683 32177 19:00
30 5 4 0.82361005 20277 12:30
31 5 5 0.84183364 35493 12:20
32 5 6 0.76399322 20782 12:30
33 6 1 0.79851028 38313 18:00
34 6 2 0.86444975 22610 20:00
35 6 3 0.81692195 21741 12:30
36 6 4 0.79744735 40004 19:30
37 6 5 0.80054485 16286 16:00
38 6 6 0.69823789 15913 15:30
39 1 1 0.22668452 110815 12:00
40 1 2 0.22724735 109432 15:30
41 1 3 0.27982408 111428 15:30
42 1 4 0.20168067 111518 12:00
43 1 5 0.19452391 111609 15:30
44 1 6 0.16226979 111347 12:10
45 1 1 0.20416814 104968 12:00
46 1 2 0.14190507 106387 12:00
47 1 3 0.22660885 105514 15:30
48 1 4 0.13633213 109316 20:00
49 1 5 0.17337627 104488 12:00
50 1 6 0.13425000 107981 15:30
51 1 7 0.12309608 110033 15:30
52 1 8 0.14648760 107379 12:00
53 2 1 0.13005902 101213 12:00
54 2 2 0.17207665 100610 12:00
55 2 3 0.22771588 104840 15:30
56 2 4 0.19871122 107638 12:00
57 2 5 0.15857571 108539 20:00
58 2 6 0.17650602 104147 15:30
59 2 7 0.20653477 102649 12:00
60 1 1 0.08315098 51424 19:30
61 1 2 0.06550388 39100 19:30
62 1 3 0.14443885 51424 19:30
63 1 4 0.18003565 50846 19:30
64 1 5 0.10875650 48602 16:30
65 1 6 0.11517672 51424 19:30
66 2 1 0.12537092 46147 18:00
67 2 2 0.19192358 48763 17:00
68 2 3 0.30943026 33529 19:30
69 2 4 0.24571429 44535 12:00
70 2 5 0.28810573 38813 12:00
71 2 6 0.14048780 36171 17:00
72 2 7 0.12976070 51424 16:30
73 3 1 0.29346388 48042 19:30
74 3 2 0.41507510 44422 13:00
75 3 3 0.48589147 48559 20:00
76 3 4 0.56826568 44559 19:30
77 3 5 0.42449726 51424 17:00
78 3 6 0.40105715 47352 17:00
79 4 1 0.57912746 40913 18:00
80 4 2 0.29139073 42856 17:30
81 4 3 0.46209386 31772 11:00
82 4 4 0.65279770 37244 19:30
83 4 5 0.51059322 39596 16:00
84 4 6 0.43333333 34671 18:00
85 5 1 0.42704626 37179 13:00
86 5 2 0.45263158 39249 16:00
87 5 3 0.58174905 33225 19:30
88 5 4 0.41216216 31464 18:00
89 5 5 0.44992526 31711 12:30
90 5 6 0.50000000 48904 13:00
91 5 7 0.44674086 37391 13:00
92 1 1 0.42931937 43500 13:30
93 1 2 0.42303173 46250 18:00
94 1 3 0.44303797 49000 13:30
95 1 4 0.43637847 60000 18:00
96 1 5 0.44019139 58000 19:00
97 1 6 0.40165441 58000 12:00
98 1 1 0.73089564 79809 20:00
99 1 2 0.74991027 72422 17:00
100 1 3 0.85296641 78306 17:00
101 1 4 0.83716421 63623 18:00
102 1 5 0.63879480 73435 16:30
103 1 6 0.78256829 76309 19:30
104 1 7 0.89491992 83602 12:30
105 5 1 0.84638301 48746 19:30
106 5 2 0.83367456 49835 19:30
107 5 3 0.89519024 45218 12:30
108 5 4 0.90120746 43181 18:30
109 5 5 0.92817982 49922 17:00
110 5 6 0.93120393 56710 12:30
111 6 1 0.79287216 54130 09:00
112 6 2 0.66666667 50775 16:30
113 6 3 0.82990991 45928 17:00
114 1 1 0.10252193 71125 13:00
115 1 2 0.11477411 71178 13:00
116 1 3 0.10577390 71875 12:30
117 1 4 0.10441767 70149 12:30
118 1 5 0.14401914 72015 12:30
119 1 6 0.10453118 72450 19:00
120 1 7 0.12658782 74549 15:30
121 2 1 0.15600394 65345 14:30
122 2 2 0.14525433 65235 16:00
123 2 3 0.23240833 65816 12:30
124 2 4 0.12817148 65351 12:30
125 2 5 0.14147910 63225 12:30
126 2 6 0.18232891 65451 12:30
127 3 1 0.14491844 57775 12:30
128 3 2 0.18322763 61183 12:30
129 3 3 0.18818565 71473 12:30
130 3 4 0.17925793 64096 12:30
131 3 5 0.16376663 60717 15:30
132 3 6 0.20824216 70713 12:15
133 4 1 0.23236741 52256 12:30
134 4 2 0.28983764 57012 12:30
135 4 3 0.29229963 58255 12:30
136 4 4 0.27884615 62656 15:30
137 4 5 0.30752334 58822 16:00
138 4 6 0.27132262 55896 12:30
139 5 1 0.29963009 70045 12:30
140 5 2 0.28233749 74927 12:30
141 5 3 0.27479407 68654 17:00
142 5 4 0.26198565 66481 16:30
143 5 5 0.26469160 61124 12:00
144 5 6 0.26849894 60005 12:30
145 5 7 0.22701504 72888 16:00
Game result (Win=1; Loss=0) Athletic Dept Profit
1 1 0
2 1 0
3 1 0
4 1 0
5 0 0
6 0 0
7 1 0
8 1 0
9 0 0
10 1 0
11 1 0
12 0 0
13 0 0
14 1 0
15 0 0
16 0 0
17 1 0
18 0 0
19 1 0
20 1 0
21 1 0
22 1 0
23 0 0
24 0 0
25 0 0
26 1 0
27 1 0
28 1 0
29 0 0
30 0 0
31 1 0
32 0 0
33 1 0
34 0 0
35 1 0
36 0 0
37 0 0
38 1 0
39 1 0
40 1 0
41 1 0
42 1 0
43 1 0
44 1 0
45 1 0
46 1 0
47 1 0
48 0 0
49 1 0
50 1 0
51 0 0
52 1 0
53 1 0
54 1 0
55 1 0
56 0 0
57 1 0
58 1 0
59 0 0
60 1 0
61 1 0
62 1 0
63 1 0
64 0 0
65 1 0
66 1 0
67 1 0
68 0 0
69 0 0
70 1 0
71 1 0
72 1 0
73 1 0
74 1 0
75 1 0
76 1 0
77 1 0
78 1 0
79 1 0
80 1 0
81 1 0
82 0 0
83 0 0
84 1 0
85 1 0
86 0 0
87 1 0
88 0 0
89 1 0
90 0 0
91 0 0
92 1 0
93 1 0
94 0 0
95 1 0
96 1 0
97 0 0
98 1 0
99 1 0
100 0 0
101 0 0
102 1 0
103 1 0
104 1 0
105 1 0
106 1 0
107 1 0
108 1 0
109 0 0
110 1 0
111 1 0
112 1 0
113 0 0
114 1 0
115 1 0
116 1 0
117 0 0
118 0 0
119 1 0
120 1 0
121 0 0
122 0 0
123 1 0
124 0 0
125 0 0
126 0 0
127 0 0
128 1 0
129 0 0
130 0 0
131 0 0
132 0 0
133 1 0
134 1 0
135 1 0
136 0 0
137 0 0
138 0 0
139 1 0
140 0 0
141 0 0
142 0 0
143 0 0
144 1 0
145 0 0
Athletic Dept Total Expenses Athletic Dept Total Revenues
1 NA NA
2 NA NA
3 NA NA
4 NA NA
5 NA NA
6 NA NA
7 NA NA
8 NA NA
9 NA NA
10 NA NA
11 NA NA
12 NA NA
13 NA NA
14 NA NA
15 NA NA
16 NA NA
17 NA NA
18 NA NA
19 NA NA
20 NA NA
21 NA NA
22 NA NA
23 NA NA
24 NA NA
25 NA NA
26 NA NA
27 NA NA
28 NA NA
29 NA NA
30 NA NA
31 NA NA
32 NA NA
33 NA NA
34 NA NA
35 NA NA
36 NA NA
37 NA NA
38 NA NA
39 NA NA
40 NA NA
41 NA NA
42 NA NA
43 NA NA
44 NA NA
45 NA NA
46 NA NA
47 NA NA
48 NA NA
49 NA NA
50 NA NA
51 NA NA
52 NA NA
53 NA NA
54 NA NA
55 NA NA
56 NA NA
57 NA NA
58 NA NA
59 NA NA
60 NA NA
61 NA NA
62 NA NA
63 NA NA
64 NA NA
65 NA NA
66 NA NA
67 NA NA
68 NA NA
69 NA NA
70 NA NA
71 NA NA
72 NA NA
73 NA NA
74 NA NA
75 NA NA
76 NA NA
77 NA NA
78 NA NA
79 NA NA
80 NA NA
81 NA NA
82 NA NA
83 NA NA
84 NA NA
85 NA NA
86 NA NA
87 NA NA
88 NA NA
89 NA NA
90 NA NA
91 NA NA
92 NA NA
93 NA NA
94 NA NA
95 NA NA
96 NA NA
97 NA NA
98 NA NA
99 NA NA
100 NA NA
101 NA NA
102 NA NA
103 NA NA
104 NA NA
105 NA NA
106 NA NA
107 NA NA
108 NA NA
109 NA NA
110 NA NA
111 NA NA
112 NA NA
113 NA NA
114 NA NA
115 NA NA
116 NA NA
117 NA NA
118 NA NA
119 NA NA
120 NA NA
121 NA NA
122 NA NA
123 NA NA
124 NA NA
125 NA NA
126 NA NA
127 NA NA
128 NA NA
129 NA NA
130 NA NA
131 NA NA
132 NA NA
133 NA NA
134 NA NA
135 NA NA
136 NA NA
137 NA NA
138 NA NA
139 NA NA
140 NA NA
141 NA NA
142 NA NA
143 NA NA
144 NA NA
145 NA NA
ggplot(data = data_clean, aes(x =S_Diversion)) +geom_histogram(bins =100, fill ="black", color ="black") +labs(title ="Histogram of Stadium Waste Diversion", x ="Variable Value", y ="Frequency")+theme_classic()
Attendance
hist(data_clean$Attendance, main ="Histogram of Attendance", xlab ="Variable Value", ylab ="Frequency")
Athletic Dept Profit
options(scipen =999)hist(data_clean$`Athletic Dept Profit`, main ="Histogram of Athletic Dept Profit", xlab ="Variable Value", ylab ="Frequency")
Athletic Dept Total Expenses
hist(data_clean$`Athletic Dept Total Expenses`, main ="Athletic Dept Total Expenses", xlab ="Variable Value", ylab ="Frequency")
Athletic Dept Total Revenues
hist(data_clean$`Athletic Dept Total Expenses`, main ="Athletic Dept Total Expenses", xlab ="Variable Value", ylab ="Frequency")
data_clean %>%ggplot(mapping =aes(x = Hour_Group, y = S_Diversion)) +geom_point() +geom_smooth(method ="lm", se =FALSE, fullrange =TRUE) +labs(title ="s_diversion vs. game time") +theme(plot.title =element_text(hjust =0.5, face ="bold"))
`geom_smooth()` using formula = 'y ~ x'
data_clean %>%ggplot(mapping =aes(x = Hour_Group, y = S_Diversion, colour =factor(Conference))) +geom_point() +geom_smooth(mapping =aes(group = Conference), method ="lm", se =FALSE, fullrange =TRUE) +labs(title ="s_diversion vs. game time (level by conference)",colour ="Conference") +theme(plot.title =element_text(hjust =0.5, face ="bold"))
data_clean %>%ggplot(mapping =aes(x = Time_Of_Day, y = S_Diversion)) +geom_point() +geom_smooth(method ="lm", se =FALSE, fullrange =TRUE) +labs(title ="s_diversion vs. game time") +theme(plot.title =element_text(hjust =0.5, face ="bold"))
`geom_smooth()` using formula = 'y ~ x'
data_clean %>%ggplot(mapping =aes(x = Time_Of_Day, y = S_Diversion, colour =factor(Conference))) +geom_point() +geom_smooth(mapping =aes(group = Conference), method ="lm", se =FALSE, fullrange =TRUE) +labs(title ="s_diversion vs. game time (level by conference)",colour ="Conference") +theme(plot.title =element_text(hjust =0.5, face ="bold"))
